Engineering Manager

The Job in a Nutshell

We are seeking an experienced Engineering Manager to lead a team of 8 to 10 engineers building systems at the heart of Mrsool's marketplace — the real-time, high-scale services that power ordering, dispatch, payments, and the experiences our millions of users and couriers rely on every day. This is a hands-on, data-driven leadership role. You will own delivery and technical direction for your team while bringing sharp analytical judgement to problems that live at the intersection of distributed systems and applied data science. You will make a direct impact on reliability, unit economics, and the growth of the platform at scale.

What You Will Do

Team Leadership: Lead, grow, and retain a team of 8–10 engineers. Own hiring, performance management, career development, and the health and delivery cadence of the team.

Technical Direction: Set technical direction for your domain — driving RFCs, design docs (HLD/LLD), and execution plans, and holding a high bar for engineering standards.

Data-Driven Decision Making: Ground team decisions in data. Define the right metrics, insist on measurement before and after changes, and use experimentation (A/B and quasi-experimental) to validate that changes actually move the numbers rather than shifting them around.

Applied Data Science Partnership: Work fluently with data scientists and ML systems — forecasting, optimisation, personalisation, and prediction — enough to challenge modelling choices, reason about trade-offs, and integrate models reliably into production systems.

System Architecture & Scale: Guide the design of highly concurrent, low-latency systems that process massive real-time throughput — event streams, microservices, and the data pipelines that feed them — while safeguarding data consistency and performance.

Operational Excellence: Own reliability and root-cause discipline for your domain. Champion staging/E2E validation, observability (tracing/metrics), and CI/CD so that issues are caught before customers feel them, and diagnosed with data when they aren't.

Cross-Functional Collaboration: Partner closely with product, operations, data science, and peer engineering squads to align on strategy and deliver seamless, well-instrumented experiences.

Strategic Evolution: Balance short-term delivery against long-term architectural stability. Identify structural improvements to how the team measures, builds, and operates, and make the case for them with evidence.